Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids

TL;DR

This paper develops an interpretive framework and a prototype instrument to identify gaps between institutional AI policies and actual academic practices, focusing on empirical and epistemic signals of policy-practice divergence.

Contribution

It introduces a novel ten-item indirect-elicitation instrument and a structured interpretive framework to surface policy-practice voids in academic AI usage.

Findings

Identified three indicators of policy-practice voids: AI assessment capacity, sector-level necessity, and ontological stance.

Demonstrated the instrument's ability to surface divergence signals in academic AI practices.

Provided insights into how academics perceive and integrate generative AI in their work.

Abstract

As generative AI diffuses through academia, policy-practice divergence becomes consequential, creating demand for auditable indicators of alignment. This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use. The framework extracts empirical and epistemic signals from academics, yielding three filtered indicators of such voids: (1) AI-integrated assessment capacity (proxy) - within a three-signal screen (AI skill, perceived teaching benefit, detection confidence), the share who would fully allow AI in exams; (2) sector-level necessity (proxy) - among high output control users who still credit AI with high contribution, the proportion who judge AI capable of challenging established disciplines; and (3) ontological stance - among respondents who judge AI different in kind from prior tools, report practice change, and pass a metacognition gate, the split between material and immaterial views as an ontological map aligning procurement claims with evidence classes.